Investigation on resource scheduling with coexistence of unicast and multicast services in LTE OFDMA system

Abstract

Multicasting is an effective method of transmitting the group messages that results in minimal utilization of network resources. But in real scenario, both unicast and multicast services are to be served by the evolved Node B (eNB). So an appropriate resource allocation method should be adopted to satisfy the Quality of Service (QoS) requirements of these services. This paper focuses on resource allocation method for Long Term Evolution (LTE) downlink when these services are scheduled by the eNB. To improve the system throughput, an objective function is formulated that maximizes the throughput of both unicast and multicast services. The multicast group is in turn divided into subgroups and the subgrouping allocation is carried out by means of Genetic Algorithm (GA) that aims to maximize the total throughput. It is shown that multicast with subgrouping shows 20% increase in the total throughput and 3% reduction in average delay when compared with combined unicast and conventional multicasting method that uses Least Channel Gain (LCG) user rate for multicast transmission.

Keywords

Multicasting LTE subgroup genetic algorithm

1. Introduction

The growing demand of various broadband multimedia services like video streaming, interactive gaming and mobile television has drawn great attraction among users. The explosive growth of multicast traffic with various Quality of Service (QoS) requirements is supported by Third Generation Partnership Project (3GPP) Long Term Evolution (LTE) through evolved Multimedia Broadcast and Multicast Services (eMBMS) [1]. eMBMS delivers the multicast messages to multiple users simultaneously with a portion of resources required by normal unicast service to those users. It uses the Resource Blocks (RBs) encoded with same Modulation and Coding Scheme (MCS).

LTE is Internet protocol based architecture that supports variety of applications. It provides flexibility in spectrum, where the transmission bandwidth can be selected between 1.4 MHz and 20 MHz. The radio base station named evolved Node B (eNB) is incharge of assigning portions of spectrum shared among users. The network architecture of LTE consists of core network called Evolved Packet Core (EPC) and access network called Evolved Universal Terrestrial Radio Access Network (EUTRAN). The available transmission bandwidth is divided into number of RBs in time and frequency domain. A RB is the smallest allocation unit in LTE that can be modulated independently.

Orthogonal Frequency Division Multiple Access (OFDMA) is one of the promising technologies used by LTE in downlink for high data rate transmission of next generation wireless broadband networks. It divides the entire bandwidth into number of orthogonal subcarriers, where resource allocation is performed by exploiting the multiuser diversity gain of User Equipment (UE) to improve the overall system performance. However, resource allocation in multicasting is a challenging task due to varied channel qualities among UEs.

In literature, many scheduling mechanisms and resource allocation methods are developed in LTE for unicast services to improve the performance of the system [4,10–16,19,25]. In [12], the extensive study of downlink scheduling algorithms for unicast services is carried out. In [11], the authors analyzed the well known proportional fairness scheduler to obtain the cell throughput. Whereas, in [25], the authors developed a novel two level scheduling algorithm for unicast services to achieve the QoS of real time services. In [19], the authors proposed the scheduling method for unicast services by introducing the concept of virtual queue. Since there is increase in the demand on delay sensitive applications, the authors in [4] proposed resource allocation method with cross layer optimization approach. Whereas in [10], the authors proposed the scheduling method for unicast services, to support the system in the overload state. A queue aware resource allocation algorithm is proposed in [15] which adopts cross layer design approach to guarantee QoS. A new scheduling approach is designed for unicast services in [13,14], that is suitable for real time services by calculating separate tag values for real time and non real time services. In [16], the authors proposed scheduling methods to achieve the throughput gain for real time services and in [9], the resource allocation method is proposed to achieve the QoS by efficient cross layer scheduling scheme.

The radio resource management procedure for unicast services accounts the channel condition and QoS requirements of an individual user. The channel quality of users in downlink is estimated by the Channel Quality Indicator (CQI) reporting from the users to the eNB. This reporting method is used to choose the correct MCS for the downlink transmission of that user [2]. But in multicast resource allocation method, the individual CQI reporting method cannot be directly adopted for choosing the correct MCS for that multicast group.

The conventional method of resource allocation in multicasting is to choose the MCS corresponding to the Least Channel Gain (LCG) user of that group. This approach maximizes the fairness among multicast users but fails in effective utilization of spectrum. This method of resource allocation in multicasting becomes more inefficient when there is less number of users with poor channel condition. These issues in the conventional method motivated the researches to find solution for multicast resource allocation methods in OFDMA based systems [3].

Mostly the multicast transmission can be of two types: single rate transmission and multi rate transmission. In single rate transmission, all users in the group are transmitted with the same rate irrespective of their achievable data rate [8,30]. In multi rate transmission, the users can be transmitted with different data rates [18,31]. In [22], an opportunistic multicast resource allocation method is proposed, which allocates the best channel users in given Transmit Time Interval (TTI) by exploiting the multiuser diversity. But this method fails in allocating resources to the poor channel condition users in the system. In [21], a low complexity resource allocation method is proposed to improve the system throughput, while in [17] a resource allocation method is introduced for minimizing total power consumption.

A resource allocation method for multicasting is proposed in [28] for a multicarrier system which aims in maximizing system throughput and at the same time guarantees the fairness among UE. Resource allocation for multicast groups is done by the method of spectrum sharing control in [23]. Here, the individual multicast group is guaranteed with minimum number of subcarriers. All the above mentioned methods adopt single rate transmission. An alternative approach is to split the multicast users into many subgroups and adopting different transmission rates to each subgroup [5–7].

Multicast scheduling is also done for heterogeneous networks. In [29], multicast scheduling is done for two hop OFDMA relay networks. Here, the grouping of relays is done to maximize the aggregate multicast flow. In [26], multicast scheduling is examined with the additional deployment of micro Base Stations (mBS) if Macro Base Station (MBS) node got failed. mBS adapts scheduling scheme that are coordinated with neighbouring MBSs. It is shown that the effectiveness of using mBSs depends on the inter-site distance between the two MBSs. It is concluded that, when the inter-site distance between the two MBSs are medium or high the use of adaptive scheduling scheme in mBS leads to performance improvement in multicasting. In [20], the multicast resource allocation method is proposed that maximizes the throughput at the cost of coverage.

In general, most of the findings consider multicast transmission alone. But in real scenario, both unicast and multicast traffic are to be served by the eNB. So an appropriate resource allocation method should be adopted to satisfy the QoS requirements of both services. This paper focuses on resource allocation method for LTE downlink when both unicast and multicast services are present in the network. To improve the overall throughput an objective function is formulated that maximizes the throughput of both unicast and multicast services. The multicast group is divided into many subgroups and the subgrouping is carried out by means of Genetic Algorithm (GA) to maximize the total data rate.

The rest of the paper is organized as follows: Section 2 provides the considered LTE system model. The proposed combined unicast and multicast scheduling method is described in Section 3. Section 4 reports the simulation results and conclusions are drawn in Section 5.

Fig. 1.

System model.

2. System model

In this paper, a single cell downlink LTE system is considered with K active users. These users are classified into U unicast users and M multicast users that are served by one eNB to support both unicast and multicast traffic simultaneously as shown in Fig. 1. It is assumed that a user belongs to either unicast service or multicast service. For simplicity, single multicast group is considered that is divided into S subgroups. The system consists of N subcarriers which are determined by the transmission bandwidth B. The subcarriers have equal bandwidth $B_{n}$ over the entire bandwidth B. It is assumed that the subcarriers are divided between unicast and multicast users ( $N_{U}$ and $N_{M}$ ) according to the number of users using the service. All the multicast users in the system receive trace based video service and the unicast user receives either real time or non real time type of service. The links between the users and the eNB is assumed to be frequency selective. It is assumed that the eNB can differentiate the type of incoming traffic (unicast or multicast) and perfect channel state information is available at eNB. The total transmitted power of eNB is $P_{tot}$ and the power used in the subcarrier n is $P_{n}$ . In LTE OFDMA system, the available bandwidth is divided into multiple RBs based on time and frequency domain. In frequency domain, the RB consists of 12 consecutive subcarriers and in time domain it lasts for 1 ms duration. The antenna configuration considered in this paper is SISO, but this method can be very well extend to MIMO configuration also. The main aim of combined unicast and multicast resource scheduling is to maximize the overall throughput of the system with delay and Bit Error Rate (BER) constraints.

3. Proposed scheduling method

As discussed in literature, real scenarios contain both unicast and multicast services that are to be served by the eNB. In recent years, multicast applications like video streaming, distance education, interactive games etc are becoming very popular. In multicast transmission, the multimedia data are transmitted to many users simultaneously. However, they are transmitted at LCG rate that results in capacity limitation problems. This section attempts to provide resource scheduling method for combined unicast and multicast traffic in downlink LTE network. Here, the multicast group is divided into many subgroups based on GA to maximize the total throughput of the system.

3.1. Problem formulation

The total system throughput optimization problem for combined unicast and multicast system is formulated as follows $\begin{array}{l} (1) & Maximize & R_{T} = \sum_{i = 1}^{U} R_{i} (t) + \sum_{s = 1}^{S} R_{s} (t) \\ (2) & Such that & \sum_{n = 1}^{N} p_{n} ⩽ P_{Tot} \\ (3) & {BER}_{s, n} ⩽ {BER}_{min} \\ (4) & P {τ_{s} (t) > D_{s}} < P {L_{s}} \\ (5) & where & \sum_{s = 1}^{S} X_{s} = M and U + M = K \end{array}$ where, $R_{T}$ is the total data rate of the system, $R_{i} (t)$ and $R_{s} (t)$ are the total data rate of unicast user i and subgroup s, respectively. $P_{n}$ represents the power on subcarrier n and $P_{Tot}$ represents the total transmit power. ${BER}_{s, n}$ indicates average BER of subgroup s on subcarrier n and ${BER}_{\min}$ indicates minimum BER to meet the QoS. $τ_{s} (t)$ , $D_{s}$ and $P {L_{s}}$ represent average transmission delay of subgroup s at time t, statistical delay bound and target delay bound violation probability, respectively.

Here the optimization problem is formulated such that at any instant of time the total rate achieved by the unicast and multicast users should maximize the total rate of the system. Equation (2) ensures that the total transmit power on all the subcarriers should be less than that of total power at the eNB. Equation (3) provides minimum BER constraint for subgroup formation Equation (4) gives the delay constraint for subgroup formation. Equation (5) shows that the total number of users in all the subgroups should be equal to the number multicast users and the total number of unicast and multicast users should be equal to the number of active users in the system. At every instant of time, the throughput of unicast users is calculated initially and then the multicast throughput is calculated to achieve maximum system throughput (as in Equation (1)).

3.2. Unicast resource scheduling

To increase the throughput of the unicast users, three level scheduling algorithm is adopted that maximizes the total throughput while maintaining fairness among UEs. This algorithm works at three levels and at each level a priority metric is used to decide UE has to be allocated in the current TTI [27].

At initial level, throughput priority metric is used to select the users who are at good channel conditions. The throughput priority metric is given as $\begin{matrix} (6) & T_{i} = \frac{μ_{i, n}}{μ_{i, avg}} i \in {1, 2, \dots, U}, n \in {1, 2, \dots, N_{U}} \end{matrix}$ where, $μ_{i, n}$ is the data rate achieved by the ith user on nth subcarrier and $μ_{i, avg}$ is the average data rate achieved by the ith user on all the subcarriers. Here, the users who have throughput priority metric greater than the threshold value are chosen for allocation in the current TTI. In next level, delay priority metric is used to allocate the users who have imperative delay requirements. The delay priority metric is given as $\begin{matrix} (7) & D_{i} = \frac{1}{T_{d} + \frac{L_{i}^{j}}{r_{i}}} i \in {1, 2, \dots, U} \end{matrix}$ where, $T_{d}$ is the theoretical departure time of jth packet of user i, $L_{i}^{j}$ is the size of the packet and $r_{i}$ is the minimum data rate of that user. The users who have delay priority metric greater than the threshold value are chosen for allocation in the current TTI. At last level, buffer priority metric is used to select the users who have not served for a long time and is given as $\begin{matrix} (8) & B_{i} = \frac{S_{q \notin j, i}}{S_{T B} \times N_{i}} i \in {1, 2, \dots, U} \end{matrix}$ where, $S_{q \notin j, i}$ is the size of the qth queue excluding the jth packet of ith user, $S_{T B}$ is the transport block size and $N_{i}$ is the number of active unicast users. Based on the priority metric described above, the users are selected for transmission. The data rate achieved by the ith user on nth subcarrier can be calculated as $\begin{matrix} (9) & R_{i, n} (t) = B_{n} {log}_{2} (1 + p_{n} h_{i, n}) \end{matrix}$ where, $B_{n}$ is the subcarrier bandwidth and $p_{n}$ is the power on nth subcarrier. The total throughput of unicast user i on all the unicast subcarriers can be computed as $\begin{matrix} (10) & R_{i} (t) = \sum_{n = 1}^{N_{U}} \frac{U}{N_{U}} δ_{i, n} {log}_{2} (1 + p_{n} h_{i, n}) \end{matrix}$ where, $δ_{i, n} = {0, 1}$ is the subcarrier allocation indicator for unicast users which takes the value either zero or one. The value of subcarrier allocation indicator is one if the subcarrier n is allocated to user i and zero otherwise. The total throughput of all the unicast users in current TTI can be expressed as $\begin{matrix} (11) & R_{U} (t) = \sum_{i = 1}^{U} R_{i} (t) . \end{matrix}$

The total throughput of the unicast users can be calculated using Equation (11). Now, the multicast group is divided into subgroups by using genetic algorithm such that the total throughput achieved by the multicast and unicast users at a particular instant of time will maximize the total throughput of the system.

3.3. Multicast resource scheduling

Multicast services provide high quality multimedia information to a group of users who are located over vast geographical area. The multicast users may experience different channel quality. So an efficient resource allocation method is required to provide reliable multicast transmission. Here, multicast resource allocation is done by subgrouping the multicast group using GA such that the number of subgroups should lie between $S_{\min}$ and $S_{\max}$ . Hence the subgroup S is bounded as follows, $\begin{matrix} (12) & S_{min} ⩽ S < S_{max} . \end{matrix}$

The value of $S_{\min}$ and $S_{\max}$ is chosen by the network operator based on the nature of user’s profile. The transmission rate of each subgroup is determined by the LCG user of that subgroup. Hence Equation (9) can be formulated as follows, $\begin{matrix} (13) & R_{s, n} (t) = \frac{1}{N_{M}} {log}_{2} (1 + p_{n} h_{s, n}) \end{matrix}$ where, $N_{M}$ is the total subcarriers allocated for multicast users, $h_{s, n}$ is the channel gain of the subgroup which is determined by the LCG user of that subgroup and is given by $\begin{matrix} (14) & h_{s, n} = min_{k \in M_{s}} \frac{| h_{k, n} |^{2}}{N_{0} B_{n}} \end{matrix}$ where $B_{n}$ is the subcarrier bandwidth, $N_{0}$ is the single-sided power spectral density of white noise on each subcarrier, $h_{k, n}$ is the channel gain of the kth user in nth subcarrier.

The total throughput of particular subgroup of all the subcarriers can be given as $\begin{matrix} (15) & R_{s} (t) = \sum_{n = 1}^{N_{M}} \frac{M_{s}}{N_{M}} δ_{s, n} {log}_{2} (1 + p_{n} h_{s, n}) \end{matrix}$ where, $M_{s}$ is the number of multicast users in the subgroup and $δ_{s, n} = {0, 1}$ is the subcarrier allocation indicator for subgroups which takes the value either zero or one. The value of subcarrier allocation indicator is one if the subcarrier n is allocated to subgroup s and zero otherwise. The subgroups are formed in accordance with BER and delay constraints.

The BER of subgroup s in subcarrier n will be given as in [24] $\begin{matrix} (16) & {BER}_{s, n} = 0.2 exp (- \frac{1.6 {SNR}_{s, n}}{l_{s, n} - 1}) \end{matrix}$ where, $l_{s, n}$ is the modulation level adopted for subgroup s in nth subcarrier and ${SNR}_{s, n}$ is the signal to noise ratio of subgroup s in nth subcarrier by considering the LCG user. The BER constraint for a subgroup is formed as $\begin{matrix} (17) & {BER}_{s, n} ⩽ {BER}_{min} . \end{matrix}$

This constraint confirms that the average BER of subgroup s in nth subcarrier should be less than that of the minimum BER ( ${BER}_{\min}$ ) to meet the desired QoS. The delay constraint of the subgroup can be given as $\begin{matrix} (18) & P {τ_{s} (t) > D_{s}} < P {L_{s}} \end{matrix}$ where, $τ_{s} (t)$ is the average transmission delay of the subgroup on particular TTI, $D_{s}$ is the statistical delay bound and $P {L_{s}}$ is the target delay bound violation probability. After determining the total throughput of particular subgroup, the total throughput of all the multicast users can be determined by $(\sum_{s = 1}^{S} R_{s} (t))$ . Here, the genetic algorithm is used to determine the number of subgroups, which maximizes the total throughput of the system as stated in Equation (1) and in accordance with BER and delay constraints for subgroups.

3.4. Genetic algorithm (GA) based subgrouping

GA is an evolutionary computing method used for finding optimal solutions to the complex problems for which getting optimal solutions are complicated and time consuming. Genetic algorithm is one of the most famous problems solving tool, which is inspired by natural selection and recombination of surviving living creatures. In this paper, the genes represent the subgroup indices. During each generation (or iteration) chromosomes are evaluated using the measure of their fitness. The fitness value is nothing but the total throughput of the subgroup. The fitness value is evaluated so as to meet the objective function that is to maximize the total system throughput.

Fitness function is evaluated based on the objective function taken and the chromosomes that have higher fitness function have more chances of survival. They are termed as parents for providing next generations. Crossover is performed on the new generation to find the optimal solutions.

Initially, the multicast users are listed according to their channel condition. Subgrouping of users is done such that the number of subgroups should be within the limit as, $S_{min} ⩽ S < S_{max}$ and each subgroup should obey the constraints as in Equations (3) and (4) and each subgroup will have transmission rate equal to that of LCG user rate of that subgroup. The processes involved in GA are given as follows:

Initial group of individuals is first created randomly to generate the first population.

During each generation, the fitness is evaluated (Equation (15)) so as to attain the objective function as in Equation (1).

The best individual who has higher fitness value is used as parent to create the next generation.

New generation is created by applying crossover on the selected parents.

Mutation is applied on new generation to produce new solutions.

Fitness function is evaluated for new generation.

The process is repeated until the desired fitness value (Equation (15)) is obtained to meet the objective function (Equation (1)).

4. Performance evaluation

In this section, simulation results are presented to show the performance of the proposed resource scheduling method in terms of total throughput, packet delay and fairness index. To understand the efficiency of subgrouping, the proposed method is compared with the conventional method used for multicasting (i.e., multicasting done with LCG user rate) and with all users transmitted by unicasting method.

4.1. Simulation environment

A single cell downlink scenario is considered with fixed eNB at the centre where the users are uniformly distributed inside the cell. It is assumed that at any instant of time 40% of users receive unicast service and 60% of users receive multicast service and all the users move with a speed of 3 kmph inside the cell. It is also assumed that at any instant of time 40% of unicast users receive Voice over Internet Protocol (VoIP) flow, 40% of unicast users receive video flow and 20% of unicast users receive Best Effort (BE) service flow and multicast users receive video streaming services. For instance, for a total of 100 users in the system, 60 users receive multicast service, 16 users receive VoIP service, 16 users receive video service and 8 users receive BE service. The performance is evaluated by varying the number of users. The simulation parameters used for analysis are given in Table 1.

Table 1
Simulation parameters

Parameter Value

Bandwidth 10 MHz

Carrier frequency 2 GHz

Frame structure FDD

Number of available subcarriers 600

Subcarrier spacing 15 kHz

Channel model 6-ray Typical Urban (TU)

Path loss $128.1 + 37.6 log r (dB)$

Shadow fading Log normal, 0 mean, $σ = 8 dB$

Penetration loss 20 dB

${BER}_{\min}$ $10^{- 3}$

Mobility model Random

eNB transmission power 43 dBm

eNB coverage area 1 km

Speed of UE 3 kmph

Genetic algorithm parameters No. of population: 5

Cross over probability: 0.5

Mutation probability: 0.01

Maximum delay 0.1 s

Simulation duration 100 s

Antenna configuration SISO

Traffic model Video (Trace based (H264)), VoIP (G.729 voice flows) and BE (Infinite buffer) for unicast service and video (Trace based (H264)) for multicast service

Parameter	Value
Bandwidth	10 MHz
Carrier frequency	2 GHz
Frame structure	FDD
Number of available subcarriers	600
Subcarrier spacing	15 kHz
Channel model	6-ray Typical Urban (TU)
Path loss	$128.1 + 37.6 log r (dB)$
Shadow fading	Log normal, 0 mean, $σ = 8 dB$
Penetration loss	20 dB
${BER}_{\min}$	$10^{- 3}$
Mobility model	Random
eNB transmission power	43 dBm
eNB coverage area	1 km
Speed of UE	3 kmph
Genetic algorithm parameters	No. of population: 5
	Cross over probability: 0.5
	Mutation probability: 0.01
Maximum delay	0.1 s
Simulation duration	100 s
Antenna configuration	SISO
Traffic model	Video (Trace based (H264)), VoIP (G.729 voice flows) and BE (Infinite buffer) for unicast service and video (Trace based (H264)) for multicast service

4.2. Results and discussion

The total throughput of the system in respect of number of users is shown in Fig. 2. As LTE uses shared channel for downlink, the total system throughput decreases as the number of users increases.

Fig. 2.

Total throughput of the system.

With increase in the number of users, there is high probability of small LCG user to exist in the network that decreases the total throughput of the system and at the same time the unicast users present in the network improve the total system throughput. In overall, the combined unicast and conventional multicast transmission shows reduced system throughput. At the same time, when all the users are transmitted with unicasting method, resources get wasted for control information which decreases the throughput of the system. In the proposed resource scheduling method, subgrouping of multicast users is done by using GA. The subgroups are formed so that the throughput of the subgroups gets maximized and in addition three level scheduling method used for unicast transmission also enhances the total throughput of the system. The proposed resource scheduling method results in 20% increase in the total throughput when compared with the conventional multicasting method. It is also noted from the Fig. 3 that the average throughput of users is small for conventional multicasting method which transmits at LCG user rate when compared to subgrouping and unicast transmission method because the high channel gain users within the multicast group experience spectral inefficiency problems.

Fig. 3.

Average throughput of users.

The fairness index of users is shown in Fig. 4. Jain fairness index method is used for evaluation of fairness among users. It is noted from the figure that the conventional method achieves maximum fairness closer to 0.9 since it tries to satisfy the users who are in worst channel condition. Whereas, the proposed resource scheduling method achieves fairness closer to 0.82 since it forms subgroups for achieving maximum system throughput and the users who are in worst channel condition may suffer from the non availability of resources. When all the users are transmitted in unicasting method, the fairness level achieved by the users is closer to 0.84 which tries to maintain reasonable fairness level among users.

Fig. 4.

Fairness index of users.

Average delay experienced by the total users in the system is shown in Fig. 5. In combined unicasting and conventional multicasting method the average delay of the users increases as the number of users increases. This is due to the increase in transmission delay when multicast users in the system are served with LCG user rate. The unicast users in the system, uses three level scheduling method that tries to serve the users who need priority in terms of delay requirements. So altogether, the combined unicasting and conventional multicasting method experiences larger delay to a maximum of 11 ms for the maximum number of users (100 users) used in the simulation. When all the users are transmitted in unicasting method, the users experience maximum of 10 ms delay for the maximum number of users (100 users) used in the simulation since unicasting method gives preference to the users who are in good channel condition. In the proposed resource scheduling method, genetic algorithm based subgrouping is adopted for multicast users that has BER and delay constraints for subgroup formation. These constraints reduces the average delay experienced by the multicast users but unicast users experience increased delay due to the scheduling method used for them. So, by the combined unicasting and proposed multicasting method, users experience a reduced delay to a maximum of 9.8 ms for the maximum number of users used in the simulation, which is 3% reduction in average delay for the total users in the system when compared with conventional method used for multicasting.

Fig. 5.

Average delay of users.

The average throughput and delay experienced by the multicast users in the network are also noted and they are shown in Figs 6 and 7. They are also compared with the chunk allocation method [32] that is proposed for resource allocation among multicast groups. In this method, the allocation unit is a set of contiguous subcarriers named as chunks with constraints on total power and average BER over chunks. Such grouping of subcarriers will result in large coherence bandwidth than the subcarrier spacing. But this method addresses only the multicast resource allocation problems. But as stated earlier, the real scenario contains both unicast and multicast users in the network with different QoS requirements. So the chunk allocation method needs separate resource allocation method for unicast services.

Fig. 6.

Average throughput of multicast users.

Fig. 7.

Average delay of multicast users.

To attempt the above issue, the proposed algorithm tries to improve the overall system throughput by enhancing both unicast and multicast users’ throughput by forming an objective function and optimizing by means of GA. Thus it shows lesser throughput when compared to chunk allocation method as shown in Fig. 6. And also, it can be inferred that the multicast users will experience reduced throughput when all the users are transmitted with LCG user rate and introduces more delay to the multicast users for transmitting with LCG user rate when compared with the proposed subgrouping based method as shown in Fig. 7. And also, the proposed resource scheduling method aims in throughput maximization, which increases the average throughput of the users and the subgroup formation is done with BER and delay constraints, which reduces the average delay of the users comparable to that of chunk allocation method (Fig. 7).

5. Conclusion

In this paper, combined unicast and multicast resource allocation method is proposed to increase the overall system throughput. Multicast transmission is an efficient method for transmitting group oriented messages. Mostly, multicast transmission rate is determined by the LCG user of that group. Whereas, unicast transmission rate is decided by the user’s experienced channel quality. Real scenarios contain both types of users to exist in the system. To increase the overall system throughput, an optimization problem is formulated, in which the throughput of unicast users is determined initially and then the multicast throughput is calculated to achieve maximum system throughput. Here, the multicast group is in turn divided into subgroups and the subgrouping allocation is done by means of genetic algorithm. It is shown that this method shows 20% increase in the total throughput and 3% reduction in average delay of the total users when compared with combined unicast and conventional multicasting method. The conventional multicasting uses LCG user rate for multicast transmission.

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